Computing and Networks – serie

Exploring the field of generative artificial intelligence (GenAI) and its use in the processing and security of multimedia content, this co-authored book addresses the critical needs and emerging challenges in the rapidly evolving intersection of artificial intelligence, multimedia content, and cybersecurity. The capabilities of sophisticated GenAI models in generating, improving, and manipulating multimedia information, including images, videos, and audio are thoroughly explored. Coverage also extends to technical innovations including advanced neural network topologies, novel training methods, and methods for boosting GenAI-generated content quality and authenticity.The book's focus on security and privacy concerns and the weaknesses brought on by GenAI includes data privacy challenges, the development of unlicensed material, and the exploitation of AI for nefarious intentions. Furthermore, the ethical implications and legal obstacles involved in guaranteeing the secure and accountable implementation of generative AI in multimedia applications are examined and potential future directions identified.Generative AI for Multimedia Content Processing, Security and Privacy: Fundamentals, advances and applications will be a useful reference for multimedia researchers and engineers working with GenAI, particularly those interested in security and privacy challenges. Cybersecurity researchers and engineers working on digital content security and synthetic media prevention, AI researchers and engineering and technology practitioners developing generative AI technologies, will all find relevant information in this volume. The book will also be of interest to regulatory and compliance experts and policy makers working in the field.

Many IT projects are doomed before the ink is dry on the contract. In this highly readable book, John Smith sets out the 40 root causes of troubled IT projects and explains how these can be avoided at each stage of the project life cycle.The coverage of opportunity management, solution generation and proposal preparation will help IT services Vendors increase sales and Buyers become more effective at proposal evaluation. A proven approach to reviewing projects in delivery is presented to ensure that projects stay on track.The book goes on to describe an approach to 'turning around' a troubled project. Key to this - and a theme throughout the book - is the achievement of a high level of engagement and trust between Vendor and Buyer.Illustrated with real-life examples, key questions, and checklists, this practical handbook, is for buyers and sellers of IT services - for project sponsors, stakeholders, project managers, QA professionals and 'troubleshooters' - who want to maximise the success of their projects, their careers and their organizations.

Modern computing systems of all kinds accumulate various data at an almost unimaginable rate. Alongside the advances in technology that make such storage possible has grown a realisation that buried within this mass of data there may exist some knowledge of considerable value. This could be information critical for a company's business success or something leading to a scientific or medical discovery or breakthrough. Most data is simply stored and never examined, but machine-learning technology has the potential to extract knowledge of value (i.e. data mining).This book considers knowledge discovery - which has been defined as 'the extraction of implicit, previously unknown and potentially useful information from data' - and data mining. Six chapters examine technical issues of considerable practical importance to the future development of this field; issues such as how to overcome feature interaction problems, analysis of outliers, rule discovery, the use of background knowledge, temporal patterns and online analysis processing. There then follow six chapters which describe applications in fields as diverse as medical and health information, meteorology, organic chemistry and the electricity supply industry.The book grew from a colloquium held in 1998 by the IEE, co-sponsored by the British Computer Society Specialist Group on Expert Systems (BCS-SGES), the Society for Artificial Intelligence and Simulation of Behaviour (AISB) and the International Society for Artificial Intelligence and Education (AIED). The chapters have been expanded considerably from papers presented, and all have been fully refereed.

This book describes some of the developments in Command, Control and Communication (C3) systems. The topics cover the design of large real-time man-machine systems, which are now a vital area of intensive scientific and financial investment. C3 systems are for complex resource management and planning, and although this has a predominantly military connotation, similar systems are now developing in civil sector applications, public utilities and banking.Topics discussed include the design and structure of C3 systems, databases, standards, the man-machine interface, and advanced processing, including the sensor data fusion and artificial intelligence. It is the multifaceted nature of C3 that this book seeks to capture. The subject is too vast to survey comprehensively but this text offers the reader an important insight into this critically important aspect of modern technology.

Software engineering products are becoming ever more complex, because software is being used in the solution of more technically difficult problems and because the size of software projects continues to grow. A key question for software engineers is how to cope with this complexity. There is no single answer, and software engineers must attack the problem from a number of perspectives. A technical perspective reveals new paradigms for system development, including two paradigms that show particular promise: object-oriented design and formal methods. A management perspective recognises that good project management is vital to the success of a complex project. The scale of modern projects makes it essential to use automated support, so software engineers must also look at how to manage complexity from an automation perspective. Automated support can come in the form of discrete tools for particular tasks, or a packaged set of tools in a support environment.The papers in this book address the problem of managing complexity in software engineering from these different perspectives. The papers are grouped into three sections: Paradigms of software development; Project management; and Automated support. The papers are selected from those presented at the 4th European Seminar on Industrial Software Technology organised by the European Workshop on Industrial Computer Systems (EWICS), whose aim is to promote the effective use of industrial computer systems through education, the dissemination of information and the development of guidelines and standards.

Up until a few years ago there were many different modelling languages available to software developers. However, this vast array of choice only served to hinder communication and as a result the Unified Modelling Language (UML) was born. Although the UML has its roots firmly in the software world, the benefits of adopting a standard visual notation have been recognised in many other fields, not least of which is the field of systems engineering. This book concentrates on systems-based applications, rather than the traditional software applications that are more usually associated with the UML. Now fully updated to reflect the changes to UML for its version 2.0 release, this new edition has been substantially re-written and includes new material on systems architectures and life cycle management.

There is a growing interest in the development and deployment of surveillance systems in public and private locations. Conventional approaches rely on the installation of wide area CCTV (Closed Circuit Television), but the explosion in the numbers of cameras that have to be monitored, the increasing costs of providing monitoring personnel and the limitations that humans have to maintain sustained levels of concentration severely limit the effectiveness of these systems. Advances in information and communication technologies, such as computer vision for face recognition and human behaviour analysis, digital annotation and storage of video, transmission of video/audio streams over wired and wireless networks, can potentially provide significant improvements in this field.The book consists of a coherent selection of extended versions of presentations made in two successful IEE symposia on Intelligent Distributed Surveillance Systems (IDSS). It surveys recent development in distributed intelligent surveillance systems and brings together the work of researchers and engineers, system integrators and managers of public and private organisations likely to use such systems.

As computers are increasingly embedded, ubiquitous and wirelessly connected, security becomes imperative. This has led to the development of the notion of a 'trusted platform', the chief characteristic of which is the possession of a trusted hardware element which is able to check all or part of the software running on this platform. This enables parties to verify the software environment running on a remote trusted platform, and hence to have some trust that the data sent to that machine will be processed in accordance with agreed rules.This new text introduces recent technological developments in trusted computing, and surveys the various current approaches to providing trusted platforms. It also includes application examples based on recent and ongoing research. The core of the book is based on an open workshop on Trusted Computing, held at Royal Holloway, University of London, UK.

Development of computer science techniques has significantly enhanced computational electromagnetic methods in recent years. The multi-core CPU computers and multiple CPU work stations are popular today for scientific research and engineering computing. How to achieve the best performance on the existing hardware platforms, however, is a major challenge. In addition to the multi-core computers and multiple CPU workstations, distributed computing has become a primary trend due to the low cost of the hardware and the high performance of network systems. In this book we introduce a general hardware acceleration technique that can significantly speed up FDTD simulations and their applications to engineering problems without requiring any additional hardware devices.

This book combines the teaching of the MATLAB® programming language with the presentation and development of carefully selected electrical and computer engineering (ECE) fundamentals. This is what distinguishes it from other books concerned with MATLAB®: it is directed specifically to ECE concerns. Students will see, quite explicitly, how and why MATLAB® is well suited to solve practical ECE problems.This book is intended primarily for the freshman or sophomore ECE major who has no programming experience, no background in EE or CE, and is required to learn MATLAB® programming. It can be used for a course about MATLAB® or an introduction to electrical and computer engineering, where learning MATLAB® programming is strongly emphasized. A first course in calculus, usually taken concurrently, is essential. The book will also serve EE or CE professionals who need to learn MATLAB® and who prefer learning via examples directly relevant to their work.The distinguishing feature of this MATLAB® book is that about 15 per cent develops ECE fundamentals gradually, from very basic principles. Because these fundamentals are interwoven throughout, MATLAB® can be applied to solve relevant, practical problems. The plentiful, in-depth example problems to which MATLAB® is applied were carefully chosen so that results obtained with MATLAB® also provide insights about the fundamentals.

The practice of Model-based Systems Engineering is becoming more widely adopted in industry, academia and commerce and, as the use of modelling matures in the real world, so the need for more guidance on how to model effectively and efficiently becomes more prominent. This book describes a number of systems-level 'patterns' (pre-defined, reusable sets of views) that may be applied using the systems modelling language SysML for the development of any number of different applications and as the foundations for a system model.Topics covered include: what is a pattern? Interface definition pattern; traceability pattern; test pattern; epoch pattern; life cycle pattern; evidence pattern; description pattern; context pattern; analysis pattern; model maturity pattern; requirements modelling; expanded requirements modelling; process modelling; competence modelling; life cycle modelling; defining patterns; and using patterns for model assessment, model definition and for model retro-fitting.This book forms a companion volume to both SysML for Systems Engineering - a model-based approach and Model-based Requirements Engineering, both published by the IET. Whereas the previous volumes presented the case for modelling and provided an in-depth overview of SysML, this book focusses on a set of 'patterns' as the basis of an MBSE model and their use in today's systems engineering community.

Big Data Analytics and Software Defined Networking (SDN) are helping to drive the management of data and usage of the extraordinary increase of computer processing power provided by Cloud Data Centres (CDCs). SDN helps CDCs run their services more efficiently by enabling managers to configure, manage, secure, and optimize the network resources very quickly. Big-Data Analytics in turn has entered CDCs to harvest the massive computing powers and deduct information that was never reachable by conventional methods.Big Data and Software Defined Networks investigates areas where Big-Data and SDN can help each other in delivering more efficient services. SDN can help Big-Data applications overcome one of their major challenges: message passing among cooperative nodes. Through proper bandwidth allocation and prioritization, critical surges of Big-Data flows can be better handled to effectively reduce their impacts on CDCs. Big-Data, in turn, can help SDN controllers better analyze collected network information and make more efficient decisions about the allocation of resources to different network flows.

Modern network systems such as Internet of Things, Smart Grid, VoIP traffic, Peer-to-Peer protocol, and social networks, are inherently complex. They require powerful and realistic models and tools not only for analysis and simulation but also for prediction.This book covers important topics and approaches related to the modeling and simulation of complex communication networks from a complex adaptive systems perspective. The authors present different modeling paradigms and approaches as well as surveys and case studies.With contributions from an international panel of experts, this book is essential reading for networking, computing, and communications professionals, researchers and engineers in the field of next generation networks and complex information and communication systems, and academics and advanced students working in these fields.

Systems Modelling Language (SysML) is a tailored version of the unified modelling language (UML) that meets the needs of today's systems engineering professionals and engineers. It supports the specification, analysis, design, verification and validation of a broad range of systems and systems-of-systems, including hardware, software, information, personnel, procedures, and facilities in a graphical notation.SysML for Systems Engineering: A model-based approach provides a comprehensive overview on how to implement SysML and Model-based Systems Engineering (MBSE) in an organisation in order to model real projects effectively and efficiently. Topics covered include approach and concepts; SysML notation; diagramming guidelines; process and requirements modelling with MBSE; architectures and architectural frameworks with MBSE; value chain modelling; deploying MBSE; the benefits of MBSE; the 'people', the 'process' and the 'tool'; model structure and management; and model maturity. A detailed case study is included to illustrate the key concepts.Fully updated and revised to reflect the latest version of the standard (SysML 1.5, released in May 2017), this new edition also includes new chapters on the benefits of MBSE, model management, model maturity and value chain modelling.

Computing has moved away from a focus on performance-centric serial computation, instead towards energy-efficient parallel computation. This provides continued performance increases without increasing clock frequencies, and overcomes the thermal and power limitations of the dark-silicon era. As the number of parallel cores increases, we transition into the many-core computing era. There is considerable interest in developing methods, tools, architectures and applications to support many-core computing.The primary aim of this edited book is to provide a timely and coherent account of the recent advances in many-core computing research. Starting with programming models, operating systems and their applications; the authors present runtime management techniques, followed by system modelling, verification and testing methods, and architectures and systems. The book ends with some examples of innovative applications.

This book describes how smart cities can be designed with data at their heart, moving from a broad vision to a consistent city-wide collaborative configuration of activities. The authors present a comprehensive framework of techniques to help decision makers in cities analyse their business strategies, design data infrastructures to support these activities, understand stakeholders' expectations, and translate this analysis into a competitive strategy for creating a smart city data infrastructure. Readers can take advantage of unprecedented insights into how cities and infrastructures function and be ready to overcome complex challenges. The framework presented in this book has guided the design of several urban platforms in the European Union and the design of the City Data Strategy of the Mayor of London, UK.

The fields of Big Data and the Internet of Things (IoT) have seen tremendous advances, developments, and growth in recent years. The IoT is the inter-networking of connected smart devices, buildings, vehicles and other items which are embedded with electronics, software, sensors and actuators, and network connectivity that enable these objects to collect and exchange data. The IoT produces a lot of data. Big data describes very large and complex data sets that traditional data processing application software is inadequate to deal with, and the use of analytical methods to extract value from data. This edited book covers analytical techniques for handling the huge amount of data generated by the Internet of Things, from architectures and platforms to security and privacy issues, applications, and challenges as well as future directions.

The theory of computation is used to address challenges arising in many computer science areas such as artificial intelligence, language processors, compiler writing, information and coding systems, programming language design, computer architecture and more. To grasp topics concerning this theory readers need to familiarize themselves with its computational and language models, based on concepts of discrete mathematics including sets, relations, functions, graphs and logic.This handbook introduces with rigor the important concepts of this kind and uses them to cover the most important mathematical models for languages and computation, such as various classical as well as modern automata and grammars. It explains their use in such crucially significant topics of computation theory as computability, decidability, and computational complexity. The authors pay special attention to the implementation of all these mathematical concepts and models and explains clearly how to encode them in computational practice. All computer programs are written in C#.

As big data becomes increasingly pervasive and cloud computing utilization becomes the norm, the security and privacy of our systems and data becomes more critical with emerging security and privacy threats and challenges. This book presents a comprehensive view on how to advance security and privacy in big data, cloud computing, and their applications. Topics include cryptographic tools, SDN security, big data security in IoT, privacy preserving in big data, security architecture based on cyber kill chain, privacy-aware digital forensics, trustworthy computing, privacy verification based on machine learning, and chaos-based communication systems. This book is an essential reading for networking, computing, and communications professionals, researchers, students and engineers, working with big data and cloud computing.

Cloud computing has been a game changer for internet-based applications such as content delivery networks, social networking and multi-tier enterprise applications. However, the requirements for low-latency data access, security, bandwidth, mobility, and cost have challenged centralized data center-based cloud computing models, which is driving the need for the novel computing paradigms of edge and fog computing. The internet of things (IoT) focuses on discovery, aggregation, management, and acting on data originating from internet-connected devices via programmable sensors, actuators, mobile phones, surveillance cameras, routers, gateways and switches. But the aggregation of this data is expensive and can be time consuming.Traditional cloud-centric resource management models need to move towards more distributed and decentralized models so that they can cope with the challenges posed by the evolution of IoT smart devices and network solutions. However, supporting IoT data processing across cloud and edge data centers is not a trivial challenge. IoT sensing devices must be configured as a collection of data-analytics driven workflows where each node in the process can essentially run on multiple heterogeneous cloud and edge data centers.This book presents state-of-the-art interdisciplinary computing research in the application lifecycle management for internet of things in edge and cloud computing. The book addresses challenges from a distributed system perspective that includes both cyber and physical aspects. The authors aim to bring together the four paradigms of cloud and edge computing, cyber physical systems, internet of things and big data for future ICT systems.Written and edited by an international team of experts in the field, this book offers key insights to researchers, engineers, IT professionals, advanced students, postgraduate students and lecturers working in the fields of parallel and distributed computing, data mining, information retrieval, cloud, edge and fog computing, and the IoT.

The needs of future digital data and computer systems are expected to be two to three orders of magnitude larger than for today's systems, to take account of unprecedented amounts of heterogeneous hardware, lines of source code, numbers of users, and volumes of data. Ultrascale computing systems (UCS) are a solution. Envisioned as large-scale complex systems joining parallel and distributed computing systems, which can be located at multiple sites and cooperate to provide the required resources and performance to the users, these technologies will extend individual systems to provide the resources that are very much needed.Based on the research work in the COST Action IC 1305 Network for Sustainable Ultrascale Computing (NESUS) this book presents important results and methods towards achieving sustainable UCS. The authors present a wide range of emerging programming models that facilitate the task of scaling and extracting performance on continuously evolving platforms, while providing resilience and fault-tolerant mechanisms to tackle the increasing probability of failures throughout the entire software stack. These methods are needed to achieve scale handling, better programmability and adaptation to rapidly changing underlying computing architecture, data centric programming models, resilience, and energy-efficiency.

Blockchain technology is a powerful, cost-effective method for network security. Essentially, it is a decentralized ledger for storing all committed transactions in trustless environments by integrating several core technologies such as cryptographic hash, digital signature and distributed consensus mechanisms.Over the past few years, blockchain technology has been used in a variety of network interaction systems such as smart contracts, public services, Internet of Things (IoT), social networks, reputation systems and security and financial services. With its widespread adoption, there has been increased focus on utilizing blockchain technologies to address network security concerns and vulnerabilities as well as understanding real-world security implications.The book begins with an introduction to blockchains, covering key principles and applications. Further chapters cover blockchain system architecture, applications and research issues; blockchain consensuses and incentives; blockchain applications, projects and implementations; blockchain for internet of things; blockchain in 5G and 6G networks; edgechain to provide security in organization based multi agent systems; blockchain driven privacy-preserving machine learning; performance evaluation of differential privacy mechanisms in blockchain based smart metering; scaling-out blockchains with sharding; blockchain for GIS; and finally blockchain applications in remote sensing big data management and production.

The concept of autonomic computing seeks to reduce the complexity of pervasively ubiquitous system management and maintenance by shifting the responsibility for low-level tasks from humans to the system while allowing humans to concentrate on high-level tasks. This is achieved by building self-managing systems that are generally capable of self-configuring, self-healing, self-optimising, and self-protecting.Trustworthy autonomic computing technologies are being applied in datacentre and cloud management, smart cities and autonomous systems including driverless cars. However, there are still significant challenges to achieving trustworthiness. This book covers challenges and solutions in autonomic computing trustworthiness from methods and techniques to achieve consistent and reliable system self-management. Researchers, developers and users need to be confident that an autonomic self-managing system will remain correct in the face of any possible contexts and environmental inputs.The book is aimed at researchers in autonomic computing, autonomics and trustworthy autonomics. This will be a go-to book for foundational knowledge, proof of concepts and novel trustworthy autonomic techniques and approaches. It will be useful to lecturers and students of autonomic computing, autonomics and multi-agent systems who need an easy-to-use text with sample codes, exercises, use-case demonstrations. This is also an ideal tutorial guide for independent study with simple and well documented diagrams to explain techniques and processes.

With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks.This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.

Cloud computing has evolved as a cost-effective, easy-to-use, elastic and scalable computing paradigm to transform today's business models. 5G, Industrial IoT, Industry 4.0 and China-2050 frameworks and technologies are introducing new challenges that cannot be solved efficiently using current cloud architectures. To handle the collected information from such a vast number of devices and actuators, and address these issues, novel concepts have been proposed to bring cloud-like resources closer to end users at the edge of the network, a technology called edge computing.From architectures to models, technologies and applications, this book focuses on the Edge Computing paradigm due to its unique characteristics where heterogeneous devices can be equipped with decision making processes and automation procedures to carry out applications across widely geographically distributed areas.This book provides valuable insights for ICTs engineers, scientists, researchers, developers and practitioners who are involved in developing and implementing edge and cloud-based solutions ranging from sensors and actuators to cloud-based back-end systems.

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures.

By specializing in a vertical market, companies can better understand their customers and bring more insight to clients in order to become an integral part of their businesses. This approach requires dedicated tools, which is where artificial intelligence (AI) and machine learning (ML) will play a major role. By adopting AI software and services, businesses can create predictive strategies, enhance their capabilities, better interact with customers, and streamline their business processes.This edited book explores novel concepts and cutting-edge research and developments towards designing these fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking. The chapters focus on models and theoretical approaches to guarantee automation in large multi-scale implementations of AI and ML systems; protocol designs to ensure AI systems meet key requirements for future services such as latency; and optimisation algorithms to leverage the trusted distributed and efficient complex architectures.The book is of interest to researchers, scientists, and engineers working in the fields of ICTs, networking, AI, ML, signal processing, HCI, robotics and sensing. It could also be used as supplementary material for courses on AI, machine and deep learning, ICTs, networking signal processing, robotics and sensing.

Today's generative AI has been marked by the advent of neural networks, inspired by the human brain, which are trained to recognize patterns in a dataset. Once the network is trained, it can make decisions or predictions without being programmed to perform tasks. Generative AI learns from a set of data without explicit instructions and can create and generate new digital content such as text, audio and art. Recent models are beginning to overcome challenges such as computational power, data quality and training stability.This edited book on generative AI presents novel perspectives, approaches and methodologies, as well as security, ethical and legal considerations, and future trends. Topics and technologies covered include generative transformers and text generation models, generative models for human-like speech synthesis, generative AI for image synthesis, data synthesis for privacy protection, and exploration of the impact of generative AI in fields including industry 4.0, astronomy, and brain tumour detection. Two chapters offer perspectives on ethics and legality.Generative AI Unleashed: Advancements, transformative applications and future frontiers will serve as a valuable resource for researchers, engineers, advanced students and lecturers operating in the domains that are significantly impacted by generative AI.